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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 1721–1730 of 9051 papers

TitleStatusHype
Abstractive and mixed summarization for long-single documents—0
Attacking Transformers with Feature Diversity Adversarial Perturbation—0
A trust-based recommendation method using network diffusion processes—0
Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder—0
A tool for computing diversity and consideration on differences between diversity indices—0
Atom Responding Machine for Dialog Generation—0
Daleel: Simplifying Cloud Instance Selection Using Machine Learning—0
Dance Generation by Sound Symbolic Words—0
Dynamic Latent Separation for Deep Learning—0
ATM-R: An Adaptive Tradeoff Model with Reference Points for Constrained Multiobjective Evolutionary Optimization—0
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